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Consumer preferences for automobile energy-efficiency grades

Author

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  • Koo, Yoonmo
  • Kim, Chang Seob
  • Hong, Junhee
  • Choi, Ie-Jung
  • Lee, Jongsu

Abstract

Recently, increases in energy prices have made energy conservation and efficiency improvements even more essential than in the past. However, consumers experience difficulty in obtaining reliable information regarding energy efficiency, so that many countries have implemented regulations to enforce energy-efficiency grade labeling. In this study, consumer preferences regarding energy efficiency grades are analyzed by the mixed logit and MDCEV model based on the revealed preference data of past automobile purchases. Findings show that consumers rationally apply information on energy efficiency grades when purchasing automobiles. However, they tend to show inefficiency in automobile usage patterns. This study discusses political implications of energy efficiency policies as they might impact consumer behaviors of automobile purchase and usage.

Suggested Citation

  • Koo, Yoonmo & Kim, Chang Seob & Hong, Junhee & Choi, Ie-Jung & Lee, Jongsu, 2012. "Consumer preferences for automobile energy-efficiency grades," Energy Economics, Elsevier, vol. 34(2), pages 446-451.
    • Handle: RePEc:eee:eneeco:v:34:y:2012:i:2:p:446-451
    DOI: 10.1016/j.eneco.2011.12.012
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    1. Yeonbae Kim & Gicheol Jeong & Jiwoon Ahn & Jeong-Dong Lee, 2007. "Consumer preferences for alternative fuel vehicles in South Korea," International Journal of Automotive Technology and Management, Inderscience Enterprises Ltd, vol. 7(4), pages 327-342.
    2. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    3. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    4. Brownston, David & Bunch, David S. & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt7rf7s3nx, University of California Transportation Center.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    6. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    7. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    8. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    9. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    10. Ahn, Jiwoon & Jeong, Gicheol & Kim, Yeonbae, 2008. "A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach," Energy Economics, Elsevier, vol. 30(5), pages 2091-2104, September.
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    Cited by:

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    4. Acharya, Bikram & Lee, Jongsu & Moon, HyungBin, 2022. "Preference heterogeneity of local government for implementing ICT infrastructure and services through public-private partnership mechanism," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
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    6. Kim, Kyungah & Lee, Jongsu & Kim, Junghun, 2021. "Can liquefied petroleum gas vehicles join the fleet of alternative fuel vehicles? Implications of transportation policy based on market forecast and environmental impact," Energy Policy, Elsevier, vol. 154(C).
    7. Park, Yuri & Koo, Yoonmo, 2016. "An empirical analysis of switching cost in the smartphone market in South Korea," Telecommunications Policy, Elsevier, vol. 40(4), pages 307-318.
    8. Voltes-Dorta, Augusto & Perdiguero, Jordi & Jiménez, Juan Luis, 2013. "Are car manufacturers on the way to reduce CO2 emissions?: A DEA approach," Energy Economics, Elsevier, vol. 38(C), pages 77-86.
    9. HyungBin Moon & Hyunhong Choi & Jongsu Lee & Ki Soo Lee, 2017. "Attitudes in Korea toward Introducing Smart Policing Technologies: Differences between the General Public and Police Officers," Sustainability, MDPI, vol. 9(10), pages 1-17, October.

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    More about this item

    Keywords

    Mixed logit; Mixed multiple discrete-continuous extreme value; Energy efficiency grade;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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